When Are Relational Databases Used?
Relational databases have been a foundational technology for software applications for decades. But when should developers choose relational databases vs. other data storage options? In this article, we’ll dive deep into the key factors that make relational databases the optimal choice for many use cases.
What is a Relational Database
Firstly, relational databases organize data into two-dimensional tables composed of rows and columns. Unlike other data models, the columns have fixed data types defining the domain of values they can contain. Tables can be linked to each other through the use of primary keys and foreign keys and enable complex web-like relationships between data elements. This linkage gives flexibility for modeling real world entities and their connections.
The standard language for querying and manipulating relational data is Structured Query Language(SQL) which provides a declarative syntax for filtering, sorting, aggregation etc. SQL is widely adopted, with various dialects and has emerged among different database vendors. But the core language remains portable for developers.
The relational model was first proposed by E.F. Codd in 1970 as an enhancement over earlier network and hierarchical database approaches. It quickly became the dominant database paradigm, with Oracle and IBM early leaders in commercial relational database products. Over the time, open source options like MySQL and PostgreSQL became popular due to their low cost and developer friendly licensing.
Today, relational databases is used in a vast portion of business applications and software across the world. But they aren’t the right solution for every problem. Evolving big data needs and alternate data models like NoSQL provide specialized solutions when relational technology isn’t the best fit.
When Relational Databases Are Used?
With rise of other database technologies, when should developers choose relational databases? These are the key factors that make relational databases the optimal choice for usage.
Storing Data with Complex Relationships
One of the primary strengths of relational databases is modeling data with multi-faceted connections and relationships. For example, a typical e-commerce order may relate to a customer, shipping address, billing address, discount, tax status, payment, refund, cart used, and more. Relational modeling is adept at capturing these complex interconnections between domain entities in an organized fashion.
Many-to-Many Relationships
Relational databases easily handle many-to-many relationships like students connected to courses through the use of junction or linking tables. This pattern allows cleanly associating any number of items from two groups. Alternate data models struggle to represent this type of connection.
One-to-Many Relationships
One-to-many relationships like customers to orders are another area where relational data shines. The linkage can be elegantly modeled through the use of foreign keys that reference a primary key in the related table. Cascading updates and deletes make preserving data integrity simple.
Need for Data Integrity
A key priority for many applications is maintaining accuracy and consistency of the data. Relational databases provide robust mechanisms to meet these needs.
Built-in Integrity Constraints
Relational databases provide built-in integrity constraints that enforce validity and consistency rules on the data itself:
- CHECK – Validates values against logical predicates
- NOT NULL – Mandates certain columns cannot be blank
- UNIQUE – Ensures uniqueness across rows for specified columns
- PRIMARY KEY – Uniquely identifies each row
- FOREIGN KEY – Maintains referential integrity between tables
Data Normalization
Normalized data tries to minimize duplication and anomalies by breaking data out across properly related tables. Normalization leverages primary and foreign keys to connect data elements. Adhering to common normal forms like first, second, and third normal form produces robust, consistent relational data structures.
Scalability Needs
Relational databases enable predictable scaling strategies to support increased load. This gives teams options to meet growing demands of the business.
Vertical Scaling
For many databases, it’s possible to scale up on a single server by adding more CPU, memory, and storage resources. This allows supporting higher performance and requests without changing the schema.
Horizontal Scaling
Sharding or partitioning the database across multiple servers is a proven approach to distribute load at scale. A router directs requests to appropriate shards while preserving a unified logical view.
Access Control is Important
Sophisticated access control is often a mandatory enterprise requirement. Relational databases provide out-of-the-box mechanisms to restrict data access.
Granular Access Permissions
Permissions can be granted on specific database objects like tables, views, procedures, and even columns to authorized users or roles. Row level security takes this even further.
Row-Level Security
Policies can evaluate the data itself to determine whether a user is allowed to see a row. This creates opportunities for multi-tenant applications and privacy controls.
Examples of Relational Database Usage
Due to these strengths, relational databases remain the first choice for the majority of software applications that deal with structured data.
Common Business Systems
Customer relationship management (CRM), enterprise resource planning (ERP), accounting, human resources, payroll, and countless other business systems almost universally rely on relational data storage. The ACID guarantees, data integrity, and availability of scale relational databases provide underpin these mission critical company applications.
Web Applications
Relational databases like MySQL, SQL Server, and PostgreSQL handle user accounts, dynamic content, catalogs, transactions, and more for sophisticated web applications including popular content management systems and ecommerce platforms. RDBMS efficiently store structured data while powering complex application logic.
Mobile Applications
Games with leaderboards, social platforms, utility applications, and essentially any mobile apps requiring persistent data turn to relational backends to manage structured data. Developers utilize tested and scalable RDBMS along with ORMs that simplify mobile access.
Comparison to Other Database Models
There are specialized use cases where alternate database technologies may be a better fit than a relational database.
Document Databases
Document stores like MongoDB are designed for data that doesn’t conform to fixed schemas. JSON documents with variable fields and nesting make modeling less structured data simple and fast. Loosely-coupled data works better here.
Graph Databases
For data with complex, dynamic relationships like social networks or connected devices, graph databases like Neo4j offer optimized modeling and traversal through nodes and edges. Relational struggles at this flexibility.
Key-Value Stores
For the most basic data lookup by key without joins or relations, key-value stores like Redis provide blazing performance. Their simplicity and specialized use case contrasts with RDBMS.
Conclusion
Relational databases still dominate business software, web applications, and mobile apps due to their maturity, rich feature set, and versatility in handling structured data. Performance, integrity, and scalability form a compelling package. NoSQL and big data pipelines meet other needs like unstructured data. For complex entities and relationships, the relational database remains the go-to choice. Careful data modeling and testing helps the teams to select the right database technology for the job at hand. RDBMS continues to power much of our digital world.
